{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fashion-outfit-generation-for-e-commerce","title":"Fashion Outfit Generation for E-commerce","arxiv_id":"1904.00741","date":"2019-03-18","proceeding":null,"authors":["Elaine M. Bettaney","Stephen R. Hardwick","Odysseas Zisimopoulos","Benjamin Paul Chamberlain"],"abstract":"Combining items of clothing into an outfit is a major task in fashion retail.\nRecommending sets of items that are compatible with a particular seed item is\nuseful for providing users with guidance and inspiration, but is currently a\nmanual process that requires expert stylists and is therefore not scalable or\neasy to personalise. We use a multilayer neural network fed by visual and\ntextual features to learn embeddings of items in a latent style space such that\ncompatible items of different types are embedded close to one another. We train\nour model using the ASOS outfits dataset, which consists of a large number of\noutfits created by professional stylists and which we release to the research\ncommunity. Our model shows strong performance in an offline outfit\ncompatibility prediction task. We use our model to generate outfits and for the\nfirst time in this field perform an AB test, comparing our generated outfits to\nthose produced by a baseline model which matches appropriate product types but\nuses no information on style. Users approved of outfits generated by our model\n21% and 34% more frequently than those generated by the baseline model for\nwomenswear and menswear respectively.","url_abs":"http://arxiv.org/abs/1904.00741v1","url_pdf":"http://arxiv.org/pdf/1904.00741v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fashion-outfit-generation-for-e-commerce","repo_url":"https://github.com/AemikaChow/DATASOURCE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}